code: def critic_loss_fn(self, values, old_values, returns, mask):
      ## value loss
      values_clipped = torch.clamp(
          values,
          old_values - self.cliprange_value,
          old_values + self.cliprange_value,
      )
      if self.compute_fp32_loss:
          values = values.float()
          values_clipped = values_clipped.float()
      vf_loss1 = (values - returns) ** 2
      vf_loss2 = (values_clipped - returns) ** 2
      vf_loss = (
          0.5
          * torch.sum(torch.max(vf_loss1, vf_loss2) * mask)
          / mask.sum()
      )
      return vf_loss
  
imports: import torch
  import torch.nn.functional
  import time
  import deepspeed
  from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
  from deepspeed.accelerator import get_accelerator
  from dschat.utils.utils import print_rank_0
label_desc: Use image node_id 8 for calling the DeepSpeedPPOTrainer obj's underlying member method code with example usage: obj.critic_loss_fn(values, old_values, returns, mask) and returns: vf_loss